Artificial Intelligence for Learning Point-of-Care Ultrasound

Part of paid clinical trials in Stanford, California.

Sponsor
Stanford University
Study ID
NCT05900440
Status
Enrolling By Invitation

Conditions

  • Education, Medical
  • Ultrasound Imaging

Eligibility Criteria

Sex
ALL
Age
N/A - N/A
Healthy Volunteers
Accepted

Interventions

  • Ultrasound with Artificial Inteligence Engabled — OTHER
    Participants shall be randomized 1:1 to receive personal access to a handheld ultrasound device with artificial intelligence vs a device with no artificial intelligence. The groups shall not cross over in which intervention they received.
  • Ultrasound without Artificial Intelligence Enabled — OTHER
    Participants shall be randomized 1:1 to receive personal access to a handheld ultrasound device with artificial intelligence vs a device with no artificial intelligence. The groups shall not cross over in which intervention they received.

Study Details

Point-of care-ultrasonography has the potential to transform healthcare delivery through its diagnostic and therapeutic utility. Its use has become more widespread across a variety of clinical settings as more investigations have demonstrated its impact on patient care. This includes the use of point-of-care ultrasound by trainees, who are now utilizing this technology as part of their diagnostic assessments of patients. However, there are few studies that examine how efficiently trainees can learn point-of-care ultrasound and which training methods are more effective. The primary objective of this study is to assess whether artificial intelligence systems improve internal medicine interns' knowledge and image interpretation skills with point-of-care ultrasound. Participants shall be randomized to receive personal access to handheld ultrasound devices to be used for learning with artificial intelligence vs devices with no artificial intelligence. The primary outcome will assess their interpretive ability with ultrasound images/videos. Secondary outcomes will include rates of device usage and performance on quizzes.

Key Dates

Start date
Jun 1, 2021
Status verified
Apr 2025
Primary completion
Dec 30, 2026
Completion
Dec 30, 2027

Study Design

Enrollment
150 participants (estimated)
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
OTHER

Arms

  • Experimental: Artificial Intelligence Group
  • Active Comparator: Non Artificial Intelligence Group

Primary Outcome Measure

Time to acquire cardiac ultrasound images [ Time Frame: During procedure (300 seconds) ]

Locations (1)

FacilityCityStateZIPSite coordinators
Stanford University School of MedicineStanfordCalifornia95403-

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Stanford University School of Medicine· Stanford, CA

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